Verbal Autopsy in Civil Registration and Vital Statistics: The Symptom-Cause Information Archive

Clark, Samuel J.; Bratschi, Martin W.; Setel, Philip; AbouZahr, Carla; Savigny, Don de; Li, Zehang; McCormick, Tyler H.; Byass, Peter; Chandramohan, Daniel

doi:10.48550/arxiv.1910.00405

2019

DOI: 10.48550/arxiv.1910.00405

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Verbal Autopsy in Civil Registration and Vital Statistics: The Symptom-Cause Information Archive

Samuel J. Clark¹,

Martin W. Bratschi²,

Philip Setel³

et al.

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“…Fourth, causes of death are important to consider for targeted health interventions. The number of causes reported by a data source and the number of deaths observed from each cause can vary immensely depending on the context of data collection (Clark, Setel and Li, 2019). In order for the competing risks framework to hold, we must have an exhaustive list of mutually exclusive causes.…”

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A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data

Schumacher¹,

McCormick²,

Wakefield³

et al. 2020

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In order to implement disease-specific interventions in young age groups, policy makers in low-and middle-income countries require timely and accurate estimates of age-and cause-specific child mortality. High quality data is not available in countries where these interventions are most needed, but there is a push to create sample registration systems that collect detailed mortality information. Current methods that estimate mortality from this data employ multistage frameworks without rigorous statistical justification that separately estimate all-cause and cause-specific mortality and are not adaptable enough to capture important features of the data. We propose a flexible Bayesian modeling framework to estimate age-and causespecific child mortality from sample registration data. We provide a theoretical justification for the framework, explore its properties via simulation, and use it to estimate mortality trends using data from the Maternal and Child Health Surveillance System in China.

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mentioning

confidence: 99%

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data

Schumacher¹,

McCormick²,

Wakefield³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

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Verbal Autopsy in Civil Registration and Vital Statistics: The Symptom-Cause Information Archive

Cited by 1 publication

References 19 publications

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data

A flexible Bayesian framework to estimate age- and cause-specific child mortality over time from sample registration data

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